Organizations have made big bets on the power of data to drive business results. From data warehouses, to data lakes, to self-service BI, large amounts of money have been spent on infrastructures to enable data-driven decisions.

There is just one problem.

Data infrastructure alone is not enough. You need data literate professionals to analyze and use the data to realize the ROI of data infrastructure investments. Not surprisingly, data literacy is top of mind for many organizations in 2020.

In this talk you will learn how easy it is to acquire the hands-on skills with data to perform sophisticated analyses and make truly data-driven decisions.

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Everyone wants use data science or AI to have an impact. While many projects fall apart or fail to launch at all, there are many successful projects out there. What do those successful projects have in common? What can we learn from them to apply to future projects?

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Secrets management is hard. There are a lot of tools in the cloud and on prem that can help you with a single aspect of secret management. But none of them are an end-to-end solution.

With a little bit of glue, the Developer Experience team at Weave was able to create a secure, scalable, and user-friendly secret management system built on Git, Vault, GCP Cloud Key Management, and GCP Firestore.

While this solution is not publicly available, it is a great example of how you can build a powerful system built on the foundations provided by the GCP and other open source tools.

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For machine learning models, we all know more data is better. For convolutional neural networks, image augmentation provides a straightforward way to expand your training dataset, by applying simple transformations to the images you already have. In this talk I'll start by demoing the basic standard transforms using the albumentations python package, and work up to some more advanced strategies like CutMix and mixup. I will also discuss some findings of the RxRx1 kaggle competition that Recursion ran last summer, and how this demonstrated the power of these techniques when applied to our cellular image data.

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Think you need a data scientist? Tyler Folkman - Head of AI at Branded Entertainment - gives you 7 reasons why you may not want to hire one.

He recently wrote an article called "7 Reasons To Not Hire a Data Scientist." This article has gotten quite a bit of buzz, so we’re having Tyler discuss the points of his article with fellow data enthusiasts.

Link to the article here.

Join us for a lunchtime video chat about 7 Reasons Not To Hire A Data Scientist.

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